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Netlab Reference Manual rbfprior
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<H1> rbfprior
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<h2>
Purpose
</h2>
Create Gaussian prior and output layer mask for RBF.

<p><h2>
Synopsis
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<PRE>
[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</PRE>


<p><h2>
Description
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<CODE>[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</CODE> 
generates a vector
<CODE>mask</CODE>  that selects only the output
layer weights.  This is because most uses of RBF networks in a Bayesian
context have fixed basis functions with the output layer as the only
adjustable parameters.  In particular, the Neuroscale output error function
is designed to work only with this mask.

<p>The return value
<CODE>prior</CODE> is a data structure, 
with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, which
specifies a Gaussian prior distribution for the network weights in an
RBF network. The parameters <CODE>aw2</CODE> and <CODE>ab2</CODE> are all
scalars and represent the regularization coefficients for two groups
of parameters in the network corresponding to
 second-layer weights, and second-layer biases
respectively. Then <CODE>prior.alpha</CODE> represents a column vector of
length 2 containing the parameters, and <CODE>prior.index</CODE> is a matrix
specifying which weights belong in each group. Each column has one
element for each weight in the matrix, using the standard ordering as
defined in <CODE>rbfpak</CODE>, and each element is 1 or 0 according to
whether the weight is a member of the corresponding group or not. 

<p><h2>
See Also
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<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
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<p>Copyright (c) Ian T Nabney (1996-9)


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